Files
Cyrene_v2/internal/runtime/loop/options.go
T
AskaEth 38a403afca
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feat(persona,model,loop): 人格 thinking 配置 + 5 个死字段接线 + 推理旋钮(按 provider 映射)
按 DevDocs/design/01 §2.1/§2.2 与 design/04 §3.2 实现(规格含联网核过的官方依据:思考内容刻意不角色化、不写分步思考指令、思考控制只走参数级)。
- 人格 schema 新增 thinking:budgetTokens / effort / showToUser(*bool,省略=nil=保持现行为,显式 false=只落库) / mode(default|on|off→Enable 三态);非法 mode/负预算 → ErrInvalidThinking(不静默)
- 5 个死字段接上注入:personality→values→speechStyle→catchphrase→background 拼为独立段落(带【】小标题,空值不拼);**全空时输出与旧实现逐字节一致**(有专门测试)
- model.Request 新增 Thinking{ BudgetTokens, Enable } 与 ReasoningEffort;新增 model/reasoning.go:厂商识别(模型名→baseURL 兜底)、取值白名单(OpenAI 系 none…max / DeepSeek low|high|max)、非法值降级+WARN、**不支持一律 WARN 而非静默**;Anthropic 映射 thinking{budget_tokens} 并**保证 budget<max_tokens**(不足时抬高 max_tokens+WARN,正是历史上思考吃光预算→空回复那个坑)
- loop:新增 Options.ThinkingShowToUser(*bool) 仅门控 thinking_* 帧(正文/工具帧不受影响);场景级 mute 语义不变
- 补一处任务清单外但必需的接线:persona→model.Request 的 thinking 映射(否则 budget/effort/mode 会变成新的死字段);showToUser 刻意不产生 Thinking(纯呈现开关)
- 管理端:personaUpsertRequest 补 thinking DTO + 校验(400);PUT 省略时**保留已存块**(防旧客户端抹配置);写侧 yaml round-trip 已验证
- Docs/api-client v0.40→v0.41(可选请求字段)
- 测试:26 个新用例(loader 解析/拼接逐字节/映射、anthropic 非流式+流式预算约束、openai 透传/降级/丢弃、Qwen 诚实 WARN、loop 门控、DTO round-trip);3 次变异验证(省略与 false 混同 / 去掉预算约束 / 短路门控 各自 FAIL)
- 验收:go build/vet/test 全绿(含 57 包)+ linux/darwin 交叉编译 + 前端 build;唯一 FAIL 是既有 flake TestInterruptCancelsTurnAndClearsPending(3/20 新代码 vs 3/20 纯 HEAD,已归因非本次引入)
2026-10-03 19:54:22 +08:00

148 lines
8.4 KiB
Go

package loop
import (
"context"
"log/slog"
"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/agent"
"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/capacity"
"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/guardrail"
"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/memory"
"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/model"
"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/tool"
"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/trace"
)
// Options configures a Loop. All fields are optional and nil is tolerated until
// the step that actually needs the field: the agent is required for Run (Run
// returns ErrNilAgent otherwise); a missing tool dispatcher causes a requested
// call to be fed back as an error; a missing retriever/persona skips memory
// injection; a missing extractor/store skips the memory hook; a missing
// checkpoint skips persistence; a missing tracer skips tracing.
type Options struct {
// Agent is the loop's execution unit: an instruction string plus a backend
// adapter, an optional tool dispatcher and optional guardrails.
Agent *agent.Agent
// Tools is the tool dispatcher used to execute model-requested calls. When
// nil the loop falls back to the agent's own dispatcher (if any).
Tools *tool.Dispatcher
// Capacity, when non-nil and enabled, filters the tool schemas advertised to
// the model each round to the allowed set for the current capacity state
// (design/14 唤醒式能力渐进 §4): the loop only exposes the allowed set to the
// model — a view, not a hard boundary (Invoke is left unblocked so an
// emergency call still works). Nil (the default) keeps the pre-existing
// behavior: every registered tool is advertised.
Capacity *capacity.Decider
// OnCapacity, when non-nil, receives capacity_awaken transition events the
// loop observes while deciding a round (design/14 §4), so a caller can
// surface the "能力点亮" progression to the frontend. Nil = no-op.
OnCapacity func(capacity.Event)
// OnAudit, when non-nil, receives an AuditEvent every time the loop denies a
// tool call in a restricted (group/stranger) environment (design/11 §3 §8),
// so the rejection lands in the audit trail and the frontend can render the
// small "xx 工具在群聊被拒" note. Nil = the rejection is still fed back to the
// model as a soft not_allowed result, just not recorded to an external sink.
OnAudit func(AuditEvent)
// OnTurnEnd, when non-nil, is called once per settled turn (a Run that
// returned a Result) with the turn's user/assistant text and the persona the
// loop is bound to, so a caller can advance state that follows the persona —
// the emotion machine and affection curve of design/01 §2. Its return value
// is recorded on Result.Mood and carried to the persisted assistant message.
// Nil = the loop reports nothing and Result.Mood stays empty.
OnTurnEnd TurnEndHook
// Memory is the long-term memory store used to persist extracted items.
Memory *memory.Store
// Retriever injects relevant memories into the context, re-evaluated every
// round (see assembleContext) so retrieval tracks the latest conversation.
Retriever *memory.Retriever
// WorkingMemory is the per-session sliding window, provided for the
// orchestrator to inspect; the loop does not mutate it.
WorkingMemory *memory.WorkingMemory
// Extractor derives memory items to persist after every step.
Extractor memory.Extractor
// Checkpoint persists a durable snapshot of the loop state at every step and,
// on a later Run with the same sessionID, resumes an interrupted run from it
// (design §1 durable execution). *persist.Store satisfies the narrow
// checkpointStore interface; nil disables both persistence and resume.
Checkpoint checkpointStore
// Tracer emits a structured span for every step, LLM call and tool call.
Tracer *trace.Tracer
// Flow, when non-nil, accumulates the run's trace spans into a per-session
// flow graph and publishes ws.graph_update on each span transition (design/19
// §1). Nil disables the realtime flow visualization.
Flow *FlowPublisher
// Guards validates request/response payloads on the streaming path, where
// RunStep is bypassed. On the non-streaming path the agent runs its own
// guardrails inside RunStep.
Guards *guardrail.Pipeline
// PersonaID scopes memory retrieval and extraction. Blank skips both.
PersonaID string
// Timezone is the IANA zone used to render the per-round "current time"
// system message (e.g. "Asia/Shanghai"). Empty disables time injection
// entirely (backward compatible); a name that fails to load also disables it.
Timezone string
// MaxRounds is the tool-loop upper bound. Non-positive selects
// DefaultMaxRounds (8).
MaxRounds int
// OnStream, when non-nil, switches the loop to the streaming path: the model
// adapter's ChatStream is driven directly and each structured stream event is
// forwarded to this callback (model.StreamEvent), so the frontend can render
// body text, tool calls and (eventually) reasoning segments independently.
OnStream func(model.StreamEvent)
// Thinking carries the reasoning knobs (budget / explicit switch) injected
// into every model request the loop builds. nil means "do not intervene",
// which is the pre-existing behavior. It is a loop option rather than a
// persona read inside the loop so the per-turn, per-role resolution a later
// multi-character batch adds only changes the wiring-time resolver.
Thinking *model.Thinking
// ReasoningEffort is the OpenAI-compatible strength knob injected into every
// model request. Empty means "do not intervene".
ReasoningEffort string
// ThinkingShowToUser gates the thinking frames the loop broadcasts: nil (the
// default) keeps the pre-existing behavior and pushes them; an explicit false
// keeps thinking_start/delta/end out of the client stream while text and tool
// frames are unaffected. The frames are still produced and still reach the
// model/transcript paths — only the UI broadcast is gated, so display and
// persistence stay decoupled (design/04 §3.2).
ThinkingShowToUser *bool
// ToolExecution selects how a batch of tool calls is run: "parallel" (the
// default) or "sequential". Empty means parallel.
ToolExecution string
// ToolParallelism caps how many tool calls run concurrently when ToolExecution
// is "parallel". Non-positive selects DefaultToolParallelism (4).
ToolParallelism int
// Hooks installs optional per-batch/per-call tool-loop hooks. Every field is
// nil-tolerant; these are reserved for "敏感操作代审" (design/11) and the
// future asynchronous-tool path. A zero Hooks value is a no-op.
Hooks ToolLoopHooks
// MaxContextTokens is the context budget that triggers overflow compaction.
// Non-positive (the zero value) disables compaction, preserving the loop's
// previous behavior. Setting it to a positive value (e.g.
// DefaultMaxContextTokens) enables compaction: when the loop's estimated
// context exceeds the budget it summarizes older turns into a leading summary
// message while keeping the recent tail (including trailing tool results)
// intact (design §1.3). Nil-tolerant: leaving it unset changes nothing.
MaxContextTokens int
// Compactor compacts an over-budget conversation. Nil selects the built-in
// structural compactor (no model call); a non-nil value lets a caller inject
// an LLM-backed or custom summarizer. It is only invoked when
// MaxContextTokens is positive.
Compactor Compactor
// Vision, when non-nil, rewrites an image-bearing user message at the start
// of Run so the main model can reason over the image (design/06 §0). The
// concrete resolver (vision.MessageResolver) decides via the vision Router
// whether to keep the image for a vision-capable main model or fold it into a
// text description from the vision/OCR tool ("先看图再对话"). Nil keeps the
// image content as-is, passing it through to a vision-capable main model.
Vision ImageResolver
// TransformContext, when non-nil, is called immediately before every model
// step with the assembled message list (after memory injection and any
// compaction) and the current round number (1-based). It may project, filter
// or re-order the messages the model sees. It is the loop's equivalent of
// OpenClaw's transformContext (design §1.3). Nil = no projection.
TransformContext func(ctx context.Context, messages []model.Message, round int) []model.Message
// Log is the base logger. Nil is replaced with a discard logger tagged with
// source "runtime/loop".
Log *slog.Logger
}